Developing a Cognition Endpoint for Traumatic Brain Injury Clinical Trials
Bibliographic record
Abstract
Cognitive impairment is a core clinical feature of traumatic brain injury (TBI). After TBI, cognition is a key determinant of post-injury productivity, outcome, and quality of life. As a final common pathway of diverse molecular and microstructural TBI mechanisms, cognition is an ideal endpoint in clinical trials involving many candidate drugs and nonpharmacological interventions. Cognition can be reliably measured with performance-based neuropsychological tests that have greater granularity than crude rating scales, such as the Glasgow Outcome Scale-Extended, which remain the standard for clinical trials. Remarkably, however, there is no well-defined, widely accepted, and validated cognition endpoint for TBI clinical trials. A single cognition endpoint that has excellent measurement precision across a wide functional range and is sensitive to the detection of small improvements (and declines) in cognitive functioning would enhance the power and precision of TBI clinical trials and accelerate drug development research. We outline methodologies for deriving a cognition composite score and a research program for validation. Finally, we discuss regulatory issues and the limitations of a cognition endpoint.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.081 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".